Questions tagged [timeseries-segmentation]
Time series segmentation arises in time series analysis and digital signal processing. An input time-series is divided into a sequence of discrete segments in order to reveal the underlying properties of its source. Note that this is different from time series clustering!
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How to model a discontinuous Time Series with two or more "components"?
Suppose a time series clearly has two or more “components”, e.g. a “zero” component and another one that looks like a continuous series. Example:
Suppose we can’t find covariates that can explain why ...
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Segmentation 3D architectures
I would like to ask you for advice on the best solution for my problem. I have a task to segment an object based on some of its signal. That is, I have input objects of size T x H x W, where T is time....
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Normalize data with a variance-shift in measurement-error to achieve high correlation with true underlying process?
I am discussing the question whether to normalize the data or not in the following setting:
I have a true time series
$$s_t = iid(0,\sigma_s^2)$$
however I only observe different $$\hat{s}_{t,i} = s_t ...
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Golf swing segmentation from time-series data
I need to identify golf swings in a set of time-series motion capture data. The following illustrates the captured Y-values (in video pixels) of a person's left hand over a period of about 10 seconds, ...
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Flat window removal from time series
I have a time series that I'm using for forecasting and I'm facing an issue with a flat period.
In my time series, I have the following dynamic: In the past the quantity was stationary (red part), ...
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(supervised ML) approach for time series segmentation
Update prior to posting
So I had just finished writing my post and got suggested "time series segmentation" for my tags. Then I looked it up and it seems like it is the thing I need to do. I ...
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How to detect an unknown number of segments, each to be fitted with an unknown parametric curve/surface equation?
Let's say I have a set of points (possibly noisy) in an N-dimensional space that represent an arbitrary number of curved segments, each segment having an arbitrary type of curve. See the 2D sample ...
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Using propensity score methods with multilevel time series data
I wanted to understand whether it would be feasible to use propensity score matching/weighting/stratification on my data. I'm investigating a region of the world where a number of countries joined an ...
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How to train a Deep Neural Network to predict conditional probability distribution?
I am working with non-stationary time series data sampled at 128 Hz. I have segmented the entire time series into 1-second segments, meaning now I have a bunch of vectors, say $x_i$, where i = 1,2,3,.....
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Clustering Data with Time and ~10 million records
I have a dataset with features like product categories, their dimensions, price, units sold on a given day.
I want to create clusters out of this dataset (~12-15 million records) and I am using data ...
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1D cluster - Jenks optimization - Finding optimal number
I have a sample data variable shown below
score
10,
11,
12,
90,
95,
97,
38,
37,
35
Instead of applying/binning data based on ...
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Are Hidden Markov Models the right tool for signal segmentation task?
I have a particular problem, and I would like to know if using a HMM is the correct tool for it. Apologies for the poor wording of the problem, HMMs are definitely not my specialty.
I have the ...
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Dividing a data set into segments with consistent inner behavior, using segmentation algorithms and metrics for consistency
Context of the problem:
I have signal data which was recorded in a software system and which shows the runtime of multiple processes over time. In total there are more than 900 processes each having ...
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Estimating duration of temporary exogenous predictor cox proportional hazard model
I have time-to-event survival data (ie., start, end, fate [death or censor] for each known individual).
I am looking to model survival for a population of animals that are released onto a new ...
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Robustly extracting subpatterns from time-series data
As part of an experiment, I need to collect time-series samples which are tightly associated with some input data. I send this data to an external device, and then collect the associated trace using ...
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Detecting household showing abnormal behavior
I am trying to figure out those household which have abnormal pattern in viewing one particular youtube channels. The data is of 12 weeks
The households may fall into one of this category and many ...
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Choosing a changepoint detection algorithm
I've been reading up on changepoint algorithms (dynamic programming, Bayesian Online Changepoint detection, Hidden Markov Models, etc.) and am looking to implement an algorithm that has a certain set ...
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Which statistical techniques can be used to decide which definition of sets produces the most coherent grouping of data
To explain. I am a historian, and an almost complete statistical novice.
I am interested in exploring the ways in which generational
alignments might be identified, not via use of generational labels, ...
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Need help specifying my problem: time series segmentation with clustering?
I can't get any further with my segmentation/clustering/classification problem and need help in choosing the right tools, or rather in leading me to the right problem definition.
I have a single long ...
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Acceptable level of mAP in computer vision applied to health applications
EDIT:
This question is meant for those who previously understand what mAP means but for contextualizing this question properly, it is the mean average precision as defined by Microsoft COCO i.e. the ...
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Data balancing in image classification
I've to segment defects from an image. The image consists of only tomatoes with it's defects in it. The defects and tomatoes in the dataset are as follows:
...
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Meaning of sparse annotation for images?
what is sparse annotation? Is it pixel-wise labeling for images. what are the other types of annotation?
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what is the best approach in dealing with large dimension custom data for training and predicting deep learning models
i am trying to implement semantic segmentation for satellite images.My custom dataset has dimensions(height,width)in range (3000, 3000)what is the best approach for feeding(for training) and ...
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Deciding length of units in sound recognition for training HMMs
I am working on creating a method to detect changes from one song to another. Namely, I hope to use a Hidden Markov Model (HMM) in order to model a part of a song and check to see if it accurately ...
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Detection of music note sequence in audio signal
I have an audio signal which contains the combination of different western music notes(I know this combination in advance) and I want to identify the sequence of the music notes present in it. For ...
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How to use Matrix Profile for dimension reduction and clustering
Matrix Profile (MP) has been used for clustering time-series segments. In the slideshow tutorial featured on the MP website, they use a figure to demonstrate projecting segment similarity onto an M-...
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How to perform segmentation on multivariate time series?
Given a multivariate time series, which method could be applied to segment it into partitions of stationary signals.
To give more context: I am given a set of acceleration signals in XYZ-Direction. ...
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Determine the n largest intervals of noise in an audio file
I have audio files that contain interviews with long periods of silence.
n - Number of interviews for a given audio file.
I need to split the audio into periods where the interviews are actually ...
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k means for segmenting time series [closed]
Now, I am trying to understand how to segment a multivariate time series using k- means. I understand that the basic concept is to use centroids of segments rather than centroids of data points and ...
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segmentation of univariate irregular time series
this is my first post. I have an irregular time series that exhibits large shifts in both mean and in the direction of the trend. It looks something like this (though this is far cleaner than ...
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How can I programmatically detect segments of a data series to fit with different curves?
Are there any documented algorithms to separate sections of a given dataset into different curves of best fit?
For example, most humans looking at this chart of data would readily divide it into 3 ...
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Clustering data into bins of variable sizes
I'd like to build a model (in R or excel) that takes in large amount of linear data and segments it into "bins". The linear data is an attribute that reflects what condition that section/record is at.
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Goodness of fit of linear model for Segmentation of GPS positions time series
I have some GPS coordinates series taken in regular time steps and I need to verify whether some chunks of the trajectories fit well as a straight line or not. The aim is to perform segmentation on ...
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Differences between clustering and segmentation
I have read about piecewise aggregate approximation (PAA) mining time series data, sliding window, top down and bottom up approaches for time series segmentation but these are applicable to single ...
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How to identify spikes in a noisy time series?
I have time-series data of brain cell spiking. It's basically got a baseline of random noise with large spikes interspersed. I want to be able to algorithmically cluster the spike portions of the ...
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Hidden Markov Model segmentation of different proportions of binary data
I need to segment a sequence of 0s and 1s by their proportion at relatively large scales. As an example, let's define 5 different states that represent 5 different ratios of 1s & 0s.
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strucchange breakpoints command: does NA mean no breaks are identified?
I am using the package strucchange to analyze a monthly time series. I read it in as a zoo object. The series looks something like this:
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